{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "logp = -3,398.7, ||grad|| = 1,989: 100%|███████████████████████████████████████████████| 7/7 [00:00<00:00, 2334.99it/s]\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "Metropolis: [mu]\n",
      "Sampling 2 chains, 0 divergences: 100%|█████████████████████████████████████| 41000/41000 [00:11<00:00, 3514.34draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\pymc3\\plots\\__init__.py:21: UserWarning: Keyword argument `varnames` renamed to `var_names`, and will be removed in pymc3 3.8\n",
      "  warnings.warn('Keyword argument `{old}` renamed to `{new}`, and will be removed in pymc3 3.8'.format(old=old, new=new))\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\arviz\\plots\\backends\\matplotlib\\distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n",
      "  \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\arviz\\plots\\backends\\matplotlib\\distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n",
      "  \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001E5002E6908>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E5002D8FC8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#例题17.10\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymc3 as pm\n",
    "import scipy\n",
    "import scipy.stats as stats\n",
    "import scipy.optimize as opt\n",
    "import matplotlib.pyplot as plt\n",
    "def poisson_logprob(mu, sign=-1):\n",
    "    return np.sum(sign*stats.poisson.logpmf(y_obs, mu=mu))\n",
    "# 读取数据文件\n",
    "messages = pd.read_csv('data/QQ_data.csv')\n",
    "with pm.Model() as model:\n",
    "    # 创建一个概率模型\n",
    "    mu = pm.Uniform('mu', lower=0, upper=60)\n",
    "    likelihood = pm.Poisson('likelihood', mu=mu, observed=messages['numbers'].values)\n",
    "    start = pm.find_MAP()\n",
    "    step = pm.Metropolis()\n",
    "    trace = pm.sample(20000, step, start=start, progressbar=True)\n",
    "y_obs = messages['numbers'].values\n",
    "#极大似然估计求解mu\n",
    "freq_results = opt.minimize_scalar(poisson_logprob)\n",
    "#traceplot函数来绘制后验采样的趋势图\n",
    "pm.traceplot(trace, varnames=['mu'], lines={'mu': freq_results['x']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 1 0 1 0 1\n",
      " 1 0 1 1 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 1 0 0 1\n",
      " 0 0 1 0 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "# 例17.11 定义一个抛硬币问题，假设硬币正面向上的概率为0.35，生成数据样本\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import pymc3 as pm\n",
    "np.random.seed(1)\n",
    "n_experiments = 100 # 试验次数\n",
    "theta_real = 0.35 # 硬币正面向上的概率参数 θ，用 theta_real 来表示\n",
    "data = stats.bernoulli.rvs(p=theta_real, size=n_experiments)\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "logp = -69.315, ||grad|| = 14: 100%|█████████████████████████████████████████████████████| 7/7 [00:00<00:00, 31.39it/s]\n",
      "Multiprocess sampling (2 chains in 2 jobs)\n",
      "Metropolis: [theta]\n",
      "Sampling 2 chains, 0 divergences: 100%|████████████████████████████████████████| 3000/3000 [00:04<00:00, 670.06draws/s]\n",
      "The number of effective samples is smaller than 25% for some parameters.\n"
     ]
    }
   ],
   "source": [
    "with pm.Model() as our_first_model: # 构建了一个模型的容器\n",
    "    theta = pm.Beta('theta', alpha=1, beta=1) # 指定先验\n",
    "    #用跟先验相同的语法描述了似然概率，用 observed 参数传递了观测到的数据\n",
    "    y= pm.Bernoulli('y', p=theta, observed=data)\n",
    "    # 返回最大后验（Maximum a Posteriori，MAP），为采样方法提供一个初始点\n",
    "    start=pm.find_MAP()\n",
    "    # 采样方法Metropolis-Hastings 算法，PyMC3会根据不同参数的特性自动地赋予一个采样器\n",
    "    step = pm.Metropolis()\n",
    "    # 执行采样，其中第 1 个参数是采样次数，第 2 个和第 3 个参数分别是采样方法和初始点\n",
    "    trace = pm.sample(1000, step=step, start=start)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\arviz\\plots\\backends\\matplotlib\\distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n",
      "  \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n",
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\arviz\\plots\\backends\\matplotlib\\distplot.py:38: UserWarning: Argument backend_kwargs has not effect in matplotlib.plot_distSupplied value won't be used\n",
      "  \"Argument backend_kwargs has not effect in matplotlib.plot_dist\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001E0EB5E1F88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E0EB63A808>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#样本路径图\n",
    "burnin = 100\n",
    "chain = trace[burnin:]\n",
    "pm.traceplot(chain, lines={'theta': theta_real})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\pymc3\\stats\\__init__.py:43: UserWarning: gelman_rubin has been deprecated. In the future, use rhat instead.\n",
      "  warnings.warn(\"gelman_rubin has been deprecated. In the future, use rhat instead.\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    theta    float64 1.015</pre>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    theta    float64 1.015"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Gelman-Rubin 检验\n",
    "pm.gelman_rubin(chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hpd_3%</th>\n",
       "      <th>hpd_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_mean</th>\n",
       "      <th>ess_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>theta</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.461</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>313.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>1.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        mean     sd  hpd_3%  hpd_97%  mcse_mean  mcse_sd  ess_mean  ess_sd  \\\n",
       "theta  0.362  0.051   0.277    0.461      0.003    0.002     313.0   298.0   \n",
       "\n",
       "       ess_bulk  ess_tail  r_hat  \n",
       "theta     329.0     292.0   1.01  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.summary(chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001E0EBBB7688>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001E0EBBE9888>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 993.6x331.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.autocorrplot(chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\ProgramData\\Anaconda3\\lib\\site-packages\\pymc3\\stats\\__init__.py:50: UserWarning: effective_n has been deprecated. In the future, use ess instead.\n",
      "  warnings.warn(\"effective_n has been deprecated. In the future, use ess instead.\")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>&lt;xarray.DataArray &#x27;theta&#x27; ()&gt;\n",
       "array(329.01445761)</pre>"
      ],
      "text/plain": [
       "<xarray.DataArray 'theta' ()>\n",
       "array(329.01445761)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pm.effective_n(chain)['theta']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001E0EA3FEC48>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.plot_posterior(chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
